APPLICATION NOTE

WITec GmbH, Lise-Meitner-Str. 6, 89081 Ulm, Germany
fon +49 (0) 731 140 700, fax +49 (0) 731 140 70 200
info@WITec.de, www.WITec.de

Automated Microparticle Analysis
with ParticleScout and Raman
Microscopy

ParticleScout is an advanced analysis
tool for finding, classifying andidentifying microparticles in evenlarge sample areas. Automatedroutines sort particles according tostructural properties, identify theirchemical composition using their
Raman spectra and generate detailed
reports.

application noteLise-Meitner-Str. 6 • D-89081 Ulm, Germany
Tel. +49 (0) 731 140 700 • Fax. +49 (0) 731 140 70200
www.witec.de • info@witec.de

The Raman principle

The Raman effect is based on the inelastic
scattering of light by the molecules ofgaseous, liquid or solid materials. Theinteraction of a molecule with photons
causes vibrations of its chemical bonds,
leading to specific energy shifts in the
scattered light. Thus, any given chemicalcompound produces a particular Raman
spectrum when excited and can be easily
identified by this individual “fingerprint.”
Raman spectroscopy is a well-established,label-free and non-destructive method foranalyzing the molecular composition of asample.

No need for compromises

Raman imaging

In Raman imaging, a confocal microscopeis combined with a spectrometer anda Raman spectrum is recorded at every
image pixel. The resulting Raman image
visualizes the distribution of the sample’s
compounds. Due to the high confocality of
WITec Raman systems, volume scans and3D images can also be generated.

The Raman effect is extremely weak, so
every Raman photon is important for
imaging. Therefore WITec Raman imaging
systems combine an exceptionallysensitive confocal microscope with anultra-high throughput spectrometer(UHTS). Precise adjustment of all opticaland mechanical elements guarantees thehighest resolution, outstanding speed andextraordinary sensitivity - simultaneously!
This optimization allows the detectionof Raman signals of even weak Raman
scatterers and extremely low material
concentrations or volumes with thelowest excitation energy levels. This isan unrivaled advantage of WITec systems.

Resolution

Lateral resolution is physicallylimited to ~200 nm, dependingon the wavelength of theincident light.

Speed

The more sensitive a system is, the shorter
the acquisition time for a single spectrum.
WITec’s Ultrafast Raman Imaging reduces
acquisition times for single Raman spectra
down to well below 1 ms.

Sensitivity

A high confocality increases the signal-to-noise ratio by reducing
the background. With the UHTS Series, WITec developed lens-based,
wavelength-optimized spectrometers with a spectral resolution
down to 0.1 cm-1 relative wavenumbers.

application noteLise-Meitner-Str. 6 • D-89081 Ulm, Germany
Tel. +49 (0) 731 140 700 • Fax. +49 (0) 731 140 70200
www.witec.de • info@witec.de

Automated particle analysis with ParticleScout

High-resolution measurements of particles are of great interest in many fields of application. WITec’s ParticleScout is ananalysis tool for the alpha300 Raman microscope series that locates, categorizes, identifies and quantifies particles overeven large sample areas. Automated routines sort particles and acquire their Raman spectra, generating reports thatprovide a detailed overview of the sample.
Pollen, dust, flour, metal flakes and pigments in paints, titanium dioxide in sunscreen and toothpaste, fat crystals in foodemulsions – these and many more sub­stances in our daily lives contain or consistof microparticles. Recently, the public andscientific communities have directed theirattention towards microplastic particles inthe environment.
Confocal Raman microscopy is ideally suited to finding, classifying and identifying
microparticles because not only doesit yield images with a resolution down
to 200 nm, but with Raman vibrational

spectroscopy the chemical components ofa sample can be identified. It is a nondestructive method that requires little, if any,
sample preparation. A Raman microscope
can generate high-resolution images thatshow both the structural features anddistribution of molecules within a sample.
However, Raman spectroscopic imaging
is not yet widely applied to microparticle
analysis.
The challenge in Raman microparticle
analyses lies in automating the detec­tion of individual particles and classifying
those of interest by size or shape before

determining their chemical compositions.
For such analyses, WITec has developed
ParticleScout. In combination with a WITec
Raman microscope, this tool enables measurements that proceed from a white lightsample overview to particle detection, acquisition of Raman spectra, post-process­ing of spectra and chemical identification
to creating a final report. During thispro­ce­dure the user can define the criteria
according to which the particles shall be
investigated, such as area, perimeter, minimum/maximum Feret diameter, elon­ga­tion, equivalent diameter and many more.

application noteLise-Meitner-Str. 6 • D-89081 Ulm, Germany
Tel. +49 (0) 731 140 700 • Fax. +49 (0) 731 140 70200
www.witec.de • info@witec.de

How to identify and classify microplastic particles with ParticleScout
In order to illustrate the workflow of
microparticle analysis, a mixture of
microplastics was analyzed with an­alpha300 Raman microscope equippedwith ParticleScout. First, large-area brightfield (Fig. 1A) and dark-field (Fig. 1B) images
were recorded by image stitching. This
technique combines images from adjacentsample areas into one composite, so thathigh-resolution images of large areas canbe acquired. Additionally, focus stacking
yielded sharp outlines for all the differently sized particles by combining images
from different focal planes (Fig. 1A, B).
A software algorithm detected particles in
the overview image through a brightness
threshold and represented their positionsin the form of a two-color image (Fig. 1C).
For each particle, structural characteristics
were calculated automatically, such asarea, perimeter, aspect ratio and many
more. Conventional Raman imaging oflarge areas would include much of theempty space surrounding the sparsely
distributed particles. In order to accelerate
the measurement, ParticleScout automatically records spectra of selected particles
only (see Fig. 1D for an example).
After processing the spectra (i.e. background subtraction) the particles werechemically identified using the seamlessly-integrated TrueMatch Raman databasemanagement software. TrueMatch automatically searches commercial or custom
databases quickly and identifies particles
reliably.
Finally, a report was generated (Table 1)that summarized the abundance and physical properties of the different materials
in the sample. The relative abundanceof the sample components is illustrated
graphically (Fig. 1E).
As particle classification, image processing
and analysis of Raman spectra are executed within one platform, ParticleScout
offers an effective solution for automated,comprehensive investigations of particles.

A

B

C

D

E

Figure 1: Analysis of a mixed microplastic sample using ParticleScout.

(A)-(B): Large-area (1 mm x 1 mm) bright-field (A) and dark-field (B) views of a mixture of microplastic
particles were generated using image stitching and focus stacking.
(C): Particles are automatically detected through a brightness threshold and represented as a two-­color mask.
(D): Background-corrected Raman spectrum of an example particle.

(E): Composition of the mixed plastic sample. After processing the spectra, the chemical compositionsof the individual particles were identified using TrueMatch (see Table 1).
PS: polystyrene; POM: polyoxymethylene; PET: polyethylene terephthalate; PC: polycarbonate; PTFE:
polytetrafluoroethylene; Unknown: unidentified particles.

Table 1: Composition of a mixed microplastic sample.

Abundance and size distribution for the identified materials. See Fig. 1 for abbreviations.
Sum

5-10 µm

10-20 µm

20-50 µm

50-100 µm> 100 µm

PS

89

47

12

8

17

5

POM

59

34

12

8

4

1

PET

217

106

70

20

17

4

PC

87

18

45

17

7

0

PTFE

913

417

297

103

77

19

Unknown

150

45

78

8

19

0

Sum

1515

667

514

164

141

29

application noteLise-Meitner-Str. 6 • D-89081 Ulm, Germany
Tel. +49 (0) 731 140 700 • Fax. +49 (0) 731 140 70200
www.witec.de • info@witec.deQuantifying microplastics in environmental samples
Environmental pollution by microplastics
is a growing concern because of theirpotentially harmful effects on humanhealth and ecosystems. For assessing such
effects, microplastics in environmental
samples need to be quickly and reliably
identified and their abundance and size
distribution must be quantified. The
aim of the follow­ing measurement was
to quantify the amount of microplastic
particles in a ­sludge sample from a waste­water treatment plant (sample courtesy of
Dieter Fischer, Leibniz Institute of Polymer
Research, Dresden, Germany). The sludge
sample (50 g) was pretreated, purified
and filtered. Figure 2 shows the darkfield image of a filter (pore size 10 µm)
on which tens of thousands of particles
from the sludge sample were retained.
­ParticleScout automatically measured
Raman spectra for about 18,000 particles.
Out of these, 46 were unambiguously

identified as microplastics by the database software TrueMatch, corresponding
to about 0.25 % of all measured particles.
The most abundant types of microplastics
were polyethylene (25 particles) and poly­
propylene (12 particles). Their sizes ranged
from 10 µm to 100 µm (circular equivalentdiameter). Particles in this size range canbe ingested by diverse marine organisms,
but their potential consequences are still
subject to investigation.

Figure 2: Microplastics in a wastewater treatment plant sludge sample.

Dark-field image of a silicon filter with 10 µm
pore size (left) and zoom-in image of the area
marked in red (right). About 0.25 % of all investigated particles were microplastics.
[Sample courtesy of Dieter Fischer, Leibniz Institute of Polymer Research, Dresden, Germany.]

The five steps of microplastic analyses

A detailed microparticle analysis typically consists of the five following steps. A high levelof automation is required because manually inspecting a large number of particles istime-consuming and error-prone.
•

Collecting and processing the sample: Environmental samples have to becollected and purified for further analysis, for example by filtration or sieving.

•

Locating particles: Particles are located in a large-area white-light image.

•

Categorizing particles: Particles are grouped according to structural parameterssuch as size or shape.

•

Identifying particles: Raman spectroscopy is well suited to investigating thechemical composition of microplastic particles.

•

Generating a report: Tables and histograms summarize the sample composition
and relate chemical to structural properties.

Further reading: Microplastics

Araujo et al. Identification of microplastics
using Raman spectroscopy: Latest developments and future prospects. Water Res. 2018,
142: p. 426-440.

Hidalgo-Ruz et al. Microplastics in the marine
environment: a review of the methods used for
identification and quantification. Environ. Sci.
Technol. 2012, 46: p. 3060-3075.

Ivleva et al. Microplastic in Aquatic Ecosystems.
Angew. Chem. Int. Ed. 2017, 56: p. 1720-1739.
Käppler et al. Analysis of environmental microplastics by vibrational microspectroscopy: FTIR,
Raman or both? Anal. Bioanal. Chem. 2016, 408:
p. 8377-8391.
Van Cauwenberghe et al. Microplastics in sediments: A review of techniques, occurrence andeffects. Mar. Environ. Res. 2015, 111: p. 5-17.

application noteLise-Meitner-Str. 6 • D-89081 Ulm, Germany
Tel. +49 (0) 731 140 700 • Fax. +49 (0) 731 140 70200
www.witec.de • info@witec.de

Microparticles in a cosmetic cream
In some pharmaceutical and cosmetic
products, microparticles are responsible
for the desired effects or consistency. Here,a cosmetic peeling cream was analyzed
using an alpha300 R microscope e­ quippedwith ParticleScout. First, a large-area
image was generated by image stitching(section shown in Fig. 3A). In the brightfield image, crystalline particles are clearlyvisible as bright blue structures, while
the cream matrix appears as dark grey. A
complete Raman image was acquired fora subsection of the image and overlaid,visualizing the spatial distribution of thesample components. The Raman image
is color coded according to the recorded
spectra of the identified components(Fig. 3B), showing that the cream consists mainly of anatase and boron nitride
particles in an oil matrix. Anatase is a formof titanium dioxide and causes the peeling
effect, while boron nitride is often used incosmetics as a slip modifier.
In the next step, ParticleScout was used
to analyze the cream’s composition in
more detail. Raman spectra were acquired
automatically for 3941 particles. With theseamlessly-integrated TrueMatch software, the recorded Raman spectra were
processed and the particles were identified by referencing the Raman database.
Quantification of the sample components
revealed 37 % anatase and 31 % boron
nitride particles in the cream (Fig. 3C). The
particles were further categorized according to their physical shape and size using
Boolean filters. For example, the size distributions of the anatase and boron nitride
particles were compared (Fig. 3D). For thishistogram, the projection area was used asa measure for the particle size, but otherparameters such as perimeter, boundingbox, Feret diameter, aspect ratio or circularequivalent diameter could also be used forsimilar analyses.

A

B

C

D

Figure 3: Particles in a cosmetic peeling cream.

(A): Optical bright-field image overlaid with the confocal Raman image (color coded according to thespectra in B).
(B): Raman spectra of the molecular components in the sample: anatase (orange), boron nitride(green) and oil (blue).

(C): Pie chart of the compound distribution in the sample: 37 % anatase (orange), 31 % boron nitride(green), 17 % oil (blue), 15 % not identified (grey).
(D): Area distribution of the anatase (orange) and boron nitride (green) particles.

application noteLise-Meitner-Str. 6 • D-89081 Ulm, Germany
Tel. +49 (0) 731 140 700 • Fax. +49 (0) 731 140 70200
www.witec.de • info@witec.de

Particle analysis in pharmaceutical samples
Qualitative and quantitative microparticle analyses are of interest for research,development and quality control in thepharmaceutical industry, as the efficacy
and safety of drugs depend on the composition and particle size of the substances
contained [1].
Here, an analgesic (pain-killing) and anti­
pyretic (anti-fever) powder sample wasinvestigated with ParticleScout by firstacquiring a dark-field overview image. In
order to exclude aggregates from the investigation, only particles with a diameterof less than 100 µm were selected and Raman spectra were automatically ac­quired
from more than 3000 particles. Due toParticleScout’s integration time optimiza­tion feature, many hundreds of particles
could be analyzed per hour. The alpha300
microscope used for the experiment wasable to target even very small particles
for the Raman spectrum acquisition andAparticles with an area down to 1 µm2 wereconsidered for the measurement. After
identifying the particles by their Raman
spectra, a quantitative report describing
the proportions and size distributions of
the sub­stances was generated. The presented pie charts compare the percentageof particles (Fig. 4A) and the area fractions(Fig. 4B) for each substance. The majorityof the particles were the analgesic agents­acetaminophen and ethenzamide. Other
substances were detected, including
caffeine as an adjuvant for improved
pain relief, lactose as a carrier, and white
pigment.
Figure 4: Raman-based particle analysis of apharmaceutical powder.

A

Percentage

BArea
fraction

Percentage (A) and area fraction (B) for theidentified particles: Acetaminophen (blue, 1340
particles), ethenzamide (cyan, 1154 particles), lactose (green, 308 particles), white pigment (pink,
155 particles), and caffeine (orange, 95 particles).

B

C

D

Figure 5: Raman analysis of a painkiller tablet.

(A): High-resolution, large-area Raman image of the tablet’s surface, color coded accord­
ing to the spectra in (B): acetaminophen (blue), aspirin (purple), caffeine (red), magne­
sium stearate (yellow), starch (green), lactose (cyan). (1 x 1 mm2 with 1000 x 1000 pixels.)(B): Corresponding Raman spectra.

(C): Individual Raman images of caffeine (left, red) and lactose (right, cyan) used foranalysis with ParticleScout.
(D): Grain size distributions for lactose (cyan) and caffeine (red).

Caffeine

Lactoseapplication noteLise-Meitner-Str. 6 • D-89081 Ulm, Germany
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In addition to the characterization of
powders, analyses of other sample forms
can also profit from the capabilities of­ParticleScout. In pharmaceutical ­tablets,
the grain size of an ingredient can influ­
ence the tablet’s properties. Here, ahigh-resolution, large-area Raman imageof a painkiller tablet’s surface was recorded (Fig. 5A). It shows the distribution ofits main chemical compounds color coded
according to their Raman spectra (Fig. 5B).

The main analgesic ingredients wereacetaminophen and aspirin. The tablet
also contained caffeine as an adjuvant,magnesium stearate as a lubricant andlactose and starch as fillers and binders.
Both caffeine and lactose were present
mostly in small defined spots, but ­caf­feine
seemed to be found in slightly larger
grains. By analyzing the Raman imagesof the individual components (Fig. 5C)with ParticleScout, quantitative grain

size distributions could be obtained. For
the caffeine and lactose identified on
the tablet surface, ParticleScout locatedhundreds of individual grains and sorted
them according to size. The area size distributions for both components reveal that
lactose indeed formed smaller grains on
average than caffeine (Fig. 5D). More than
90% of the lactose grains, but only 24% of
the caffeine grains, showed areas smaller
than 50 µm2.

Detailed analysis of selected particle subsets: WS2 nanowires
In many applications, only a fraction of
the particles in a sample is of scientificinterest. Particles in mixed samples must
therefore be selected according to physicalproperties and further analysis will thenbe limited to those that meet the specified
criteria. The following example shows how
ParticleScout can be used to isolate particles of interest quickly and conveniently.
The sample contained tungsten disulfide(WS2) nanowires, prepared by Reshef Tenne(Weizmann Institute, Israel) and kindly
provided through Martin Konečný andA

B

Tomáš Šikola (CEITEC, Institute of PhysicalEngineering, Brno University of Technology, Czech Republic). Using an alpha300 R
microscope equipped with ParticleScout,several thousand particles were located on
the silicon dioxide substrate, but not all ofthem were the desired nanowires (Fig. 6A).
These structures are several micrometers
long, but only a few hundred nanometers
thick. Manually inspecting all particles andselecting the nanowires would be tediousand time-consuming. Using ParticleScout,the desired nanowires were isolated

within seconds by their elongated shape:
Specifying an aspect ratio of greater than
2.5 yielded 218 nanowires (Fig. 6B), which
could be further investigated. For example,
Raman spectra could be acquired fromevery particle to confirm that they consistof WS2. Measurements with different laser
polarizations demonstrated the anisotrop­
ic scattering behavior of the nanowires:
The intensity of the Raman shift peak at
421 cm-1 depends on the orientation of the
nanowire with respect to the polarizationof the excitation light (Fig. 6C).

C

Figure 6: ParticleScout distinguishes WS2 nanowires from globular particles within seconds.

(A): Representative subset of the 3135 particles of less than 5 µm in length detected in a sample of WS2 nanowires on silicon dioxide.
(B): Representative subset of the 218 nanowires isolated by additionally specifying an aspect ratio of greater than 2.5.

(C): Raman spectra for two orientations of nanowires with respect to the polarization of the laser light. The intensity at 421 cm-1 depends on the angle
between the nanowire and the laser polarization.

[Sample courtesy of Reshef Tenne (Weizmann Institute, Israel), Martin Konečný and Tomáš Šikola (CEITEC, Institute of Physical Engineering, Brno Universityof Technology, Czech Republic).]application noteLise-Meitner-Str. 6 • D-89081 Ulm, Germany
Tel. +49 (0) 731 140 700 • Fax. +49 (0) 731 140 70200
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Food analysis with ParticleScout: Particulate baking ingredients
Many ingredients for baking and cooking
are particulate, for example flour, sugar,salt, baking powder, semolina, starch andmany spices, and their size and distribu­
tion influence the macroscopic propertiesof food products. In drinks such as beer, theanalysis of haze particles is an importanttask [2]. Comprehensive particle analyses
are thus relevant for research, development and quality control in the food anddrink industries. Apart from character­iz­
ing particulate products, the detectionof microplastic particles is important as
microplastics are becoming increasingly
prevalent and their potentially harmful
effects on humans and animals continue
to be investigated [3].

Here we demonstrate particle analysis
with Raman spectroscopy using a WITecalpha300 Raman microscope equippedwith ParticleScout. For this purpose, amixture of typical particulate baking
ingredients was spread on a cover slide
and wide-field images were acquired
at different sample positions. The darkfield image of one area is shown in
Fig. 7A. Raman spectra were recorded
automatically for the particles in all
images and identified using the integrated
TrueMatch database management
software. Fig. 7B shows the same sample
area as Fig. 7A, but with the analyzed
particles color-coded according to theirchemical identities. As the different

particle types have different average sizes,
their abundance is represented by theirarea fraction in Fig. 7C. Starch and smaller
oligo- and polysaccharides, which are themain components of flour, account for two
thirds of the mixture. Sugar and vanillasugar represent about 15% and baking
powder 5%. The remaining 5% include, forexample, some proteins. More detailed
analyses of the particles’ shapes andsizes would of course be possible after
measuring a more statistically significant
number of particles.

A

B

References[1] T.F. Haefele, K. Paulus, Confocal Raman
microscopy in pharmaceutical development. In:
Confocal Raman Microscopy, J. Toporski, T. Dieing,
and O. Hollricher (Editors) Springer International
Publishing AG, 2nd edition (2018) p. 381-419.
DOI: 10.1007/978-3-319-75380-5_16[2] E.-M. Kahle et al. (2020) Identification anddifferentiation of haze substances using Raman
microspectroscopy. J. Inst. Brew. 126: 362 – 370.
DOI: 10.1002/jib.627[3] N.P. Ivleva et al. (2017) Microplastic in Aquatic
Ecosystems, Angew. Chem. Int. Ed. 56: 1720 – 1739.
DOI: 10.1002/anie.201606957

Figure 7: Particle analysis with Raman spectroscopy in a mixture of baking ingredients.

(A): Dark-field image of the particles in a mixtureof typical baking ingredients.
(B): The particles located in A were color-coded
according to their Raman spectra: Starch(yellow), polysaccharides (orange), sucrose(green), vanilla sugar (blue), baking powder(cyan), others (grey). Particles at the pictureedges were not analyzed.

(C:) The components’ area fractions. In total, 139particles were identified.

C

Confocal Raman Microscopy [1]written by Raman experts
edited by WITec scientists

WITec Microscope Seriesalpha300 S: Scanning
Near-field Optical Microscopealpha300 A:
Atomic Force Microscopealpha300 R:
Confocal Raman Microscopealpha300 apyron: AutomatedConfocal Raman Microscopealpha300 Ri: Inverted
Confocal Raman Microscope

RISE: Raman Imaging –Scanning Electron Microscopealpha300 access: Confocal
Micro-Raman System

https://raman.oxinst.com
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